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Large-scale docking predicts that sORF-encoded peptides may function through protein-peptide interactions in Arabidopsis thaliana

机译:大规模对接预测,sORF编码的肽可能通过拟南芥中的蛋白-肽相互作用起作用

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摘要

Several recent studies indicate that small Open Reading Frames (sORFs) embedded within multiple eukaryotic non-coding RNAs can be translated into bioactive peptides of up to 100 amino acids in size. However, the functional roles of the 607 Stress Induced Peptides (SIPs) previously identified from 189 Transcriptionally Active Regions (TARs) in Arabidopsis thaliana remain unclear. To provide a starting point for functional annotation of these plant-derived peptides, we performed a large-scale prediction of peptide binding sites on protein surfaces using coarse-grained peptide docking. The docked models were subjected to further atomistic refinement and binding energy calculations. A total of 530 peptide-protein pairs were successfully docked. In cases where a peptide encoded by a TAR is predicted to bind at a known ligand or cofactor-binding site within the protein, it can be assumed that the peptide modulates the ligand or cofactor-binding. Moreover, we predict that several peptides bind at protein-protein interfaces, which could therefore regulate the formation of the respective complexes. Protein-peptide binding analysis further revealed that peptides employ both their backbone and side chain atoms when binding to the protein, forming predominantly hydrophobic interactions and hydrogen bonds. In this study, we have generated novel predictions on the potential protein-peptide interactions in A. thaliana, which will help in further experimental validation.
机译:最近的一些研究表明,嵌入多个真核非编码RNA中的小的开放阅读框(sORF)可以翻译成大小不超过100个氨基酸的生物活性肽。但是,先前从拟南芥中的189个转录活性区(TAR)中鉴定出的607个应激诱导肽(SIP)的功能角色仍不清楚。为了为这些植物衍生肽的功能注释提供一个起点,我们使用粗粒化肽对接对蛋白质表面上的肽结合位点进行了大规模预测。对接的模型进行了进一步的原子细化和结合能计算。总共530个肽-蛋白质对成功对接。在预测由TAR编码的肽结合在蛋白质内的已知配体或辅因子结合位点的情况下,可以假定该肽调节配体或辅因子结合。此外,我们预测几种肽在蛋白-蛋白界面结合,因此可以调节各自复合物的形成。蛋白质-肽结合分析进一步表明,肽与蛋白质结合时会同时使用其主链和侧链原子,从而形成疏水相互作用和氢键。在这项研究中,我们对拟南芥中潜在的蛋白质-肽相互作用产生了新的预测,这将有助于进一步的实验验证。

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